README.md

---
license: creativeml-openrail-m
base_model: kyujinpy/KO-anything-v4-5
training_prompt: A bear is playing guitar
tags:
- tune-a-video
- text-to-video
- diffusers
- korean
inference: false
---

# Tune-A-VideKO-anything
Github: [Kyujinpy/Tune-A-VideKO](https://github.com/KyujinHan/Tune-A-VideKO)

## Model Description
- Base model: [kyujinpy/KO-anything-v4-5](https://huggingface.co/kyujinpy/KO-anything-v4-5)
- Training prompt: A bear is playing guitar
![sample-train](bear.gif)

## Samples

![sample-500](video1.gif)
Test prompt: 1소녀는 기타를 연주하고 있다, 흰 머리, 중간 머리, 고양이 귀, 귀여운, 스카프, 재킷, 야외, 거리, 소녀

![sample-500](video2.gif)
Test prompt: 1소녀가 기타 연주를 하고 있습니다, 바다, 눈을 감음, 긴 머리, 카리스마

![sample-500](video3.gif)
Test prompt: 1소년, 기타 연주, 잘생김, 앉아있는, 빨간색 기타, 해변

## Usage
Clone the github repo
```bash
git clone https://github.com/showlab/Tune-A-Video.git
```

Run inference code

```python
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch

pretrained_model_path = "kyujinpy/KO-anything-v4-5"
unet_model_path = "kyujinpy/Tune-A-VideKO-anything"
unet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

prompt = "1소녀는 기타를 연주하고 있다, 흰 머리, 중간 머리, 고양이 귀, 귀여운, 스카프, 재킷, 야외, 거리, 소녀"
video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=12.5).videos

save_videos_grid(video, f"./{prompt}.gif")
```

## Related Papers:
- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- [Stable Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models